Jianlong Fu
Jianlong Fu
Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
J Fu, H Zheng, T Mei
Proceedings of the IEEE conference on computer vision and pattern …, 2017
6242017
Learning multi-attention convolutional neural network for fine-grained image recognition
H Zheng, J Fu, T Mei, J Luo
Proceedings of the IEEE international conference on computer vision, 5209-5217, 2017
4072017
Multi-level attention networks for visual question answering
D Yu, J Fu, T Mei, Y Rui
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
1672017
The seventh visual object tracking vot2019 challenge results
M Kristan, J Matas, A Leonardis, M Felsberg, R Pflugfelder, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
1122019
Show, adapt and tell: Adversarial training of cross-domain image captioner
TH Chen, YH Liao, CY Chuang, WT Hsu, J Fu, M Sun
Proceedings of the IEEE international conference on computer vision, 521-530, 2017
1032017
Da-gan: Instance-level image translation by deep attention generative adversarial networks
S Ma, J Fu, CW Chen, T Mei
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
1012018
Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition
H Zheng, J Fu, ZJ Zha, J Luo
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
982019
Learning pyramid-context encoder network for high-quality image inpainting
Y Zeng, J Fu, H Chao, B Guo
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
882019
Beyond Object Recognition: Visual Sentiment Analysis with Deep Coupled Adjective and Noun Neural Networks.
J Wang, J Fu, Y Xu, T Mei
IJCAI, 3484-3490, 2016
612016
Learn to scale: Generating multipolar normalized density maps for crowd counting
C Xu, K Qiu, J Fu, S Bai, Y Xu, X Bai
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
592019
Efficient clothing retrieval with semantic-preserving visual phrases
J Fu, J Wang, Z Li, M Xu, H Lu
Asian conference on computer vision, 420-431, 2012
542012
Let your photos talk: Generating narrative paragraph for photo stream via bidirectional attention recurrent neural networks
Y Liu, J Fu, T Mei, CW Chen
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
442017
Relaxing from vocabulary: Robust weakly-supervised deep learning for vocabulary-free image tagging
J Fu, Y Wu, T Mei, J Wang, H Lu, Y Rui
Proceedings of the IEEE international conference on computer vision, 1985-1993, 2015
422015
Deep attention neural tensor network for visual question answering
Y Bai, J Fu, T Zhao, T Mei
Proceedings of the European Conference on Computer Vision (ECCV), 20-35, 2018
402018
Ntire 2020 challenge on real-world image super-resolution: Methods and results
A Lugmayr, M Danelljan, R Timofte
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
362020
Beyond narrative description: Generating poetry from images by multi-adversarial training
B Liu, J Fu, MP Kato, M Yoshikawa
Proceedings of the 26th ACM international conference on Multimedia, 783-791, 2018
362018
What dress fits me best? Fashion recommendation on the clothing style for personal body shape
SC Hidayati, CC Hsu, YT Chang, KL Hua, J Fu, WH Cheng
Proceedings of the 26th ACM international conference on Multimedia, 438-446, 2018
352018
Show, reward and tell: Automatic generation of narrative paragraph from photo stream by adversarial training
J Wang, J Fu, J Tang, Z Li, T Mei
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
332018
Learning 2d temporal adjacent networks for moment localization with natural language
S Zhang, H Peng, J Fu, J Luo
Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 12870 …, 2020
322020
Ocean: Object-aware anchor-free tracking
Z Zhang, H Peng
arXiv preprint arXiv:2006.10721, 2020
312020
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